Setup

conditionsExcluded outlierHandling EFA_correlation
none keep cor

back to TOC

Data preparation

## Warning in read.table(file = file, header = header, sep = sep, quote
## = quote, : incomplete final line found by readTableHeader on '/Users/
## kweisman/Documents/Research (Stanford)/Projects/GGW-mod/ggw-mod2_moral/
## mturk/v1 follow up 1 (robot)/Batch_2292738_batch_results.csv'
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning in ifelse(as.numeric(as.character(structure(c(48L, 47L, 50L, 43L, :
## NAs introduced by coercion
## Warning in ifelse(as.numeric(as.character(structure(c(48L, 47L, 50L, 43L, :
## NAs introduced by coercion
## Warning in ifelse(as.numeric(as.character(structure(c(48L, 47L, 50L, 43L, :
## NAs introduced by coercion
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining factors with different levels, coercing to character
## vector
## Warning: joining character vector and factor, coercing into character
## vector
## Warning: joining factor and character vector, coercing into character
## vector

back to TOC

Study 1 (2015-12-15, 2 conditions, between-subjects)

Demographics

Study 1: Sample size
condition n
beetle 200
robot 205
all 405
Study 1: Study duration (minutes)
condition min_duration max_duration median_duration mean_duration sd_duration
beetle 0.77 119.12 2.77 3.92 8.50
robot 0.83 15.77 2.58 3.24 2.11
all 0.77 119.12 2.67 3.58 6.16 
## Joining by: c("condition", "min_age", "max_age", "median_age", "mean_age", "sd_age")
## Warning: joining character vector and factor, coercing into character
## vector
Study 1: Participant age (years; approximate)
condition min_age max_age median_age mean_age sd_age
beetle 19 70 31 34.24 10.64
robot 20 75 32 35.11 11.20
all 19 75 32 34.68 10.92
Study 1: Participant gender
condition m f other
beetle 119 81 0
robot 121 82 2
all 240 163 2
Study 1: Participant race/ethnicity
condition asian_east asian_other asian_south black hispanic middle_eastern multiracial native_american other_prefno pac_islander white
beetle 20 0 6 8 9 1 11 1 1 1 142
robot 16 2 2 15 9 1 9 1 1 1 148
all 36 2 8 23 18 2 20 2 2 2 290
Study 1: Participant religion
condition buddhism christianity hinduism islam judaism multireligious none other other_prefno
beetle 3 81 2 2 4 0 101 3 4
robot 2 87 0 0 5 3 100 3 5
all 5 168 2 2 9 3 201 6 9

back to TOC

Exploratory factor analysis

Run EFA with varimax rotation

## Factor Analysis using method =  minres
## Call: fa(r = d1_all, nfactors = 3, rotate = "varimax", scores = "regression", 
##     fm = "minres", cor = chosenCorType)
## Standardized loadings (pattern matrix) based upon correlation matrix
##                  MR1   MR2   MR3   h2   u2 com
## happy           0.55  0.65  0.10 0.74 0.26 2.0
## depressed       0.37  0.78  0.03 0.75 0.25 1.4
## fear            0.82  0.28  0.06 0.76 0.24 1.2
## angry           0.58  0.57  0.08 0.67 0.33 2.0
## calm            0.65  0.41  0.17 0.62 0.38 1.9
## sounds          0.06 -0.05  0.61 0.38 0.62 1.0
## seeing          0.36 -0.07  0.61 0.51 0.49 1.7
## temperature     0.21 -0.06  0.66 0.48 0.52 1.2
## odors           0.45 -0.01  0.43 0.39 0.61 2.0
## depth           0.11  0.11  0.62 0.40 0.60 1.1
## computations   -0.74  0.19  0.44 0.78 0.22 1.8
## thoughts        0.50  0.55  0.22 0.61 0.39 2.3
## reasoning      -0.06  0.47  0.57 0.55 0.45 2.0
## remembering    -0.20  0.17  0.72 0.59 0.41 1.3
## beliefs         0.11  0.76  0.12 0.60 0.40 1.1
## hungry          0.93  0.01 -0.08 0.87 0.13 1.0
## tired           0.83  0.23  0.10 0.76 0.24 1.2
## pain            0.93  0.10  0.01 0.87 0.13 1.0
## nauseated       0.65  0.50  0.09 0.68 0.32 1.9
## safe            0.70  0.36  0.13 0.63 0.37 1.6
## love            0.37  0.81  0.06 0.79 0.21 1.4
## recognizing    -0.29  0.29  0.71 0.67 0.33 1.7
## communicating  -0.02  0.20  0.65 0.46 0.54 1.2
## guilt           0.26  0.80  0.02 0.70 0.30 1.2
## disrespected    0.25  0.78  0.04 0.68 0.32 1.2
## free_will       0.70  0.37  0.09 0.63 0.37 1.5
## choices         0.25  0.18  0.60 0.46 0.54 1.5
## self_restraint  0.24  0.55  0.31 0.45 0.55 2.0
## intentions      0.54  0.35  0.27 0.48 0.52 2.3
## goal            0.09  0.17  0.62 0.42 0.58 1.2
## conscious       0.70  0.36  0.12 0.64 0.36 1.6
## self_aware      0.52  0.48  0.22 0.55 0.45 2.3
## desires         0.69  0.40  0.11 0.66 0.34 1.7
## embarrassed     0.19  0.85 -0.01 0.76 0.24 1.1
## emo_recog       0.06  0.70  0.29 0.58 0.42 1.3
## joy             0.51  0.70  0.10 0.76 0.24 1.9
## morality       -0.04  0.60  0.32 0.47 0.53 1.5
## personality     0.23  0.66  0.31 0.58 0.42 1.7
## pleasure        0.74  0.43  0.11 0.74 0.26 1.7
## pride           0.28  0.85  0.05 0.80 0.20 1.2
## 
##                        MR1  MR2  MR3
## SS loadings           9.92 9.81 5.17
## Proportion Var        0.25 0.25 0.13
## Cumulative Var        0.25 0.49 0.62
## Proportion Explained  0.40 0.39 0.21
## Cumulative Proportion 0.40 0.79 1.00
## 
## Mean item complexity =  1.6
## Test of the hypothesis that 3 factors are sufficient.
## 
## The degrees of freedom for the null model are  780  and the objective function was  36.39 with Chi Square of  14185
## The degrees of freedom for the model are 663  and the objective function was  3.81 
## 
## The root mean square of the residuals (RMSR) is  0.03 
## The df corrected root mean square of the residuals is  0.03 
## 
## The harmonic number of observations is  405 with the empirical chi square  500.89  with prob <  1 
## The total number of observations was  405  with MLE Chi Square =  1475.92  with prob <  8.7e-64 
## 
## Tucker Lewis Index of factoring reliability =  0.928
## RMSEA index =  0.057  and the 90 % confidence intervals are  0.051 0.059
## BIC =  -2504.66
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy             
##                                                 MR1  MR2  MR3
## Correlation of scores with factors             0.98 0.98 0.95
## Multiple R square of scores with factors       0.97 0.95 0.91
## Minimum correlation of possible factor scores  0.93 0.91 0.81
## Joining by: "subid"
## Warning: joining factor and character vector, coercing into character
## vector
## Joining by: "mturkcode"
## Joining by: "workerID"
## Warning: joining factor and character vector, coercing into character
## vector

back to TOC

Study 1 Moral Follow-up (robot: 2016-02-26; beetle: 2016-03-01)

Data preparation

Demographics

Study 1: Sample size
condition n
beetle 111
robot 121
all 232
Study 1: Study duration (minutes)
condition min_duration max_duration median_duration mean_duration sd_duration
beetle 0.77 23.67 2.68 3.36 3.22
robot 0.82 11.45 2.48 3.10 1.95

back to TOC

Correlations with general moral concern

## 
##  Pearson's product-moment correlation
## 
## data:  MR1 and concern_general
## t = 4.4105, df = 229, p-value = 1.587e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.156382 0.394636
## sample estimates:
##       cor 
## 0.2798115
## 
##  Pearson's product-moment correlation
## 
## data:  MR2 and concern_general
## t = 3.4838, df = 229, p-value = 0.0005921
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.09811329 0.34348073
## sample estimates:
##       cor 
## 0.2243498
## 
##  Pearson's product-moment correlation
## 
## data:  MR3 and concern_general
## t = 0.047144, df = 229, p-value = 0.9624
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1260130  0.1321399
## sample estimates:
##         cor 
## 0.003115362
## 
##  Pearson's product-moment correlation
## 
## data:  MR1 and MR2
## t = -0.13729, df = 229, p-value = 0.8909
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1379882  0.1201462
## sample estimates:
##          cor 
## -0.009072134
## 
##  Pearson's product-moment correlation
## 
## data:  MR1 and MR3
## t = 0.48221, df = 229, p-value = 0.6301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.09762977  0.16026807
## sample estimates:
##        cor 
## 0.03184926
## 
##  Pearson's product-moment correlation
## 
## data:  MR2 and MR3
## t = 0.040367, df = 229, p-value = 0.9678
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1264537  0.1316998
## sample estimates:
##       cor 
## 0.0026675
## Call: paired.r(xy = rp_MR1.gen$estimate, xz = rp_MR2.gen$estimate, 
##     yz = rp_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 0.62  With probability =  0.53
## Call: paired.r(xy = rp_MR1.gen$estimate, xz = rp_MR3.gen$estimate, 
##     yz = rp_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 3.12  With probability =  0
## Call: paired.r(xy = rp_MR2.gen$estimate, xz = rp_MR3.gen$estimate, 
##     yz = rp_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 2.42  With probability =  0.02
## Warning in cor.test.default(MR1, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR1 and concern_general
## S = 1435500, p-value = 3.123e-06
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.3012511
## Warning in cor.test.default(MR2, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR2 and concern_general
## S = 1682900, p-value = 0.005847
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.1808292
## Warning in cor.test.default(MR3, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR3 and concern_general
## S = 2162500, p-value = 0.4261
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.05261743
## Warning in cor.test.default(MR1, MR2, method = "spearman"): Cannot compute
## exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR1 and MR2
## S = 2476500, p-value = 0.001691
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.2054888
## Warning in cor.test.default(MR1, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR1 and MR3
## S = 2115200, p-value = 0.6541
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.0296376
## Warning in cor.test.default(MR2, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR2 and MR3
## S = 2106800, p-value = 0.6994
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.0255419
## Call: paired.r(xy = rs_MR1.genrank$estimate, xz = rs_MR2.genrank$estimate, 
##     yz = rs_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 1.24  With probability =  0.22
## Call: paired.r(xy = rs_MR1.genrank$estimate, xz = rs_MR3.genrank$estimate, 
##     yz = rs_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 3.9  With probability =  0
## Call: paired.r(xy = rs_MR2.genrank$estimate, xz = rs_MR3.genrank$estimate, 
##     yz = rs_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 2.51  With probability =  0.01
## 
##  Pearson's product-moment correlation
## 
## data:  MR1 and concern_general
## t = 3.1616, df = 119, p-value = 0.001991
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1050907 0.4352364
## sample estimates:
##       cor 
## 0.2783656
## 
##  Pearson's product-moment correlation
## 
## data:  MR2 and concern_general
## t = 3.042, df = 119, p-value = 0.002892
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.09464914 0.42664831
## sample estimates:
##       cor 
## 0.2686077
## 
##  Pearson's product-moment correlation
## 
## data:  MR3 and concern_general
## t = 0.0061249, df = 119, p-value = 0.9951
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1779529  0.1790401
## sample estimates:
##          cor 
## 0.0005614675
## 
##  Pearson's product-moment correlation
## 
## data:  MR1 and MR2
## t = 13.648, df = 119, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7002746 0.8422012
## sample estimates:
##       cor 
## 0.7811323
## 
##  Pearson's product-moment correlation
## 
## data:  MR1 and MR3
## t = 0.22997, df = 119, p-value = 0.8185
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1580147  0.1988248
## sample estimates:
##       cor 
## 0.0210763
## 
##  Pearson's product-moment correlation
## 
## data:  MR2 and MR3
## t = -0.52753, df = 119, p-value = 0.5988
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2248602  0.1313264
## sample estimates:
##         cor 
## -0.04830237
## Call: paired.r(xy = rp_ROBOT_MR1.gen$estimate, xz = rp_ROBOT_MR2.gen$estimate, 
##     yz = rp_ROBOT_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 0.23  With probability =  0.82
## Call: paired.r(xy = rp_ROBOT_MR1.gen$estimate, xz = rp_ROBOT_MR3.gen$estimate, 
##     yz = rp_ROBOT_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 3.11  With probability =  0
## Call: paired.r(xy = rp_ROBOT_MR2.gen$estimate, xz = rp_ROBOT_MR3.gen$estimate, 
##     yz = rp_ROBOT_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 2.89  With probability =  0
## Warning in cor.test.default(MR1, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR1 and concern_general
## S = 229350, p-value = 0.01387
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.2231781
## Warning in cor.test.default(MR2, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR2 and concern_general
## S = 209340, p-value = 0.001205
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.2909529
## Warning in cor.test.default(MR3, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR3 and concern_general
## S = 311950, p-value = 0.5376
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.05658593
## Warning in cor.test.default(MR1, MR2, method = "spearman"): Cannot compute
## exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR1 and MR2
## S = 231830, p-value = 0.018
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.2147718
## Warning in cor.test.default(MR1, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR1 and MR3
## S = 283930, p-value = 0.6765
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.03831133
## Warning in cor.test.default(MR2, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR2 and MR3
## S = 360410, p-value = 0.01498
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## -0.220733
## Call: paired.r(xy = rs_ROBOT_MR1.genrank$estimate, xz = rs_ROBOT_MR2.genrank$estimate, 
##     yz = rs_ROBOT_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = -0.86  With probability =  0.39
## Call: paired.r(xy = rs_ROBOT_MR1.genrank$estimate, xz = rs_ROBOT_MR3.genrank$estimate, 
##     yz = rs_ROBOT_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 3.13  With probability =  0
## Call: paired.r(xy = rs_ROBOT_MR2.genrank$estimate, xz = rs_ROBOT_MR3.genrank$estimate, 
##     yz = rs_ROBOT_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 3.49  With probability =  0
## 
##  Pearson's product-moment correlation
## 
## data:  MR1 and concern_general
## t = 1.1775, df = 108, p-value = 0.2416
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.07626003  0.29364023
## sample estimates:
##       cor 
## 0.1125891
## 
##  Pearson's product-moment correlation
## 
## data:  MR2 and concern_general
## t = 2.4254, df = 108, p-value = 0.01695
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.04181352 0.39759715
## sample estimates:
##       cor 
## 0.2272756
## 
##  Pearson's product-moment correlation
## 
## data:  MR3 and concern_general
## t = 1.5122, df = 108, p-value = 0.1334
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.04444539  0.32253964
## sample estimates:
##       cor 
## 0.1439944
## 
##  Pearson's product-moment correlation
## 
## data:  MR1 and MR2
## t = -1.7993, df = 108, p-value = 0.07477
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.34676327  0.01719213
## sample estimates:
##        cor 
## -0.1705985
## 
##  Pearson's product-moment correlation
## 
## data:  MR1 and MR3
## t = 17.792, df = 108, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.8066654 0.9044886
## sample estimates:
##      cor 
## 0.863485
## 
##  Pearson's product-moment correlation
## 
## data:  MR2 and MR3
## t = 0.28544, df = 108, p-value = 0.7759
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1606108  0.2135997
## sample estimates:
##        cor 
## 0.02745637
## Call: paired.r(xy = rp_BEETLE_MR1.gen$estimate, xz = rp_BEETLE_MR2.gen$estimate, 
##     yz = rp_BEETLE_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = -1.16  With probability =  0.25
## Call: paired.r(xy = rp_BEETLE_MR1.gen$estimate, xz = rp_BEETLE_MR3.gen$estimate, 
##     yz = rp_BEETLE_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = -0.92  With probability =  0.36
## Call: paired.r(xy = rp_BEETLE_MR2.gen$estimate, xz = rp_BEETLE_MR3.gen$estimate, 
##     yz = rp_BEETLE_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 0.93  With probability =  0.35
## Warning in cor.test.default(MR1, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR1 and concern_general
## S = 199790, p-value = 0.302
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.09930608
## Warning in cor.test.default(MR2, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR2 and concern_general
## S = 177680, p-value = 0.03715
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.1989936
## Warning in cor.test.default(MR3, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR3 and concern_general
## S = 2e+05, p-value = 0.3066
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.09836269
## Warning in cor.test.default(MR1, MR2, method = "spearman"): Cannot compute
## exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR1 and MR2
## S = 286800, p-value = 0.001898
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.2929481
## Warning in cor.test.default(MR1, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR1 and MR3
## S = 35168, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.8414527
## Warning in cor.test.default(MR2, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  MR2 and MR3
## S = 240360, p-value = 0.3852
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.08360158
## Call: paired.r(xy = rs_BEETLE_MR1.genrank$estimate, xz = rs_BEETLE_MR2.genrank$estimate, 
##     yz = rs_BEETLE_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = -0.96  With probability =  0.34
## Call: paired.r(xy = rs_BEETLE_MR1.genrank$estimate, xz = rs_BEETLE_MR3.genrank$estimate, 
##     yz = rs_BEETLE_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 0.03  With probability =  0.98
## Call: paired.r(xy = rs_BEETLE_MR2.genrank$estimate, xz = rs_BEETLE_MR3.genrank$estimate, 
##     yz = rs_BEETLE_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated  correlations"
## t = 1.06  With probability =  0.29

Exploratory plots

Plots with general moral concern

## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

Regressions with general moral concern

## Analysis of Variance Table
## 
## Model 1: concern_general ~ MR1 + MR2 + MR3 + condition
## Model 2: concern_general ~ (MR1 + MR2 + MR3) * condition
## Model 3: concern_general ~ (MR1 + MR2 + MR3 + condition)^2
## Model 4: concern_general ~ (MR1 + MR2 + MR3)^2 * condition
## Model 5: concern_general ~ (MR1 + MR2 + MR3)^3 * condition
## Model 6: concern_general ~ (MR1 + MR2 + MR3 + condition)^3
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    226 111315                                
## 2    223 111154  3     161.2 0.1135 0.952138   
## 3    220 104031  3    7123.2 5.0166 0.002213 **
## 4    217 103464  3     567.1 0.3994 0.753576   
## 5    215 101761  2    1702.3 1.7983 0.168065   
## 6    216 102059 -1    -297.6 0.6288 0.428680   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lm(formula = concern_general ~ (MR1 + MR2 + MR3 + condition)^2, 
##     data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -49.836 -14.847  -4.775  11.270  68.561 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         34.9356     6.5930   5.299 2.82e-07 ***
## MR1                 17.7455     6.7744   2.620 0.009419 ** 
## MR2                  5.9861     2.5214   2.374 0.018448 *  
## MR3                 -0.1649     2.7975  -0.059 0.953046    
## conditionrobot      11.3630     6.5462   1.736 0.083995 .  
## MR1:MR2            -12.1356     3.3463  -3.627 0.000357 ***
## MR1:MR3              1.0616     2.9577   0.359 0.719994    
## MR1:conditionrobot  12.4343     6.9827   1.781 0.076336 .  
## MR2:MR3              1.8989     2.7016   0.703 0.482873    
## MR2:conditionrobot -10.2120     3.7136  -2.750 0.006457 ** 
## MR3:conditionrobot   1.3525     3.8584   0.351 0.726268    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21.75 on 220 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1868, Adjusted R-squared:  0.1499 
## F-statistic: 5.055 on 10 and 220 DF,  p-value: 1.26e-06
## Analysis of Variance Table
## 
## Model 1: concern_general ~ (MR1 + MR2 + condition)^2
## Model 2: concern_general ~ (MR1 + MR2)^2 * condition
## Model 3: concern_general ~ (MR1 + MR2 + condition)^3
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    224 104328                           
## 2    223 103786  1    541.91 1.1644 0.2817
## 3    223 103786  0      0.00
## Analysis of Variance Table
## 
## Model 1: concern_general ~ (MR1 + MR2 + condition)^2
## Model 2: concern_general ~ (MR1 + MR2 + MR3 + condition)^2
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    224 104328                           
## 2    220 104031  4    296.69 0.1569 0.9597
## 
## Call:
## lm(formula = concern_general ~ (MR1 + MR2 + condition)^2, data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -47.813 -14.897  -4.131  11.159  68.378 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          35.224      5.247   6.713 1.55e-10 ***
## MR1                  16.147      5.840   2.765 0.006167 ** 
## MR2                   6.077      2.450   2.480 0.013862 *  
## conditionrobot        9.455      5.241   1.804 0.072565 .  
## MR1:MR2             -11.004      2.874  -3.829 0.000167 ***
## MR1:conditionrobot   12.299      6.011   2.046 0.041907 *  
## MR2:conditionrobot   -8.899      3.203  -2.779 0.005921 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21.58 on 224 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1845, Adjusted R-squared:  0.1627 
## F-statistic: 8.447 on 6 and 224 DF,  p-value: 2.84e-08
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

## Analysis of Variance Table
## 
## Model 1: concern_general ~ rank(MR1) + rank(MR2) + rank(MR3) + condition
## Model 2: concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3)) * condition
## Model 3: concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3) + condition)^2
## Model 4: concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3))^2 * condition
## Model 5: concern_general ~ ((rank(MR1) + rank(MR2) + rank(MR3))^3) * condition
## Model 6: concern_general ~ ((rank(MR1) + rank(MR2) + rank(MR3) + condition)^3)
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    227 111333                              
## 2    224 110964  3    369.09 0.2534 0.85885  
## 3    221 108699  3   2265.00 1.5552 0.20128  
## 4    218 106584  3   2114.86 1.4521 0.22865  
## 5    216 104864  2   1720.14 1.7716 0.17253  
## 6    217 106369 -1  -1504.51 3.0990 0.07976 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lm(formula = concern_general ~ rank(MR1) + rank(MR2) + rank(MR3) + 
##     condition, data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -42.108 -16.259  -5.906  11.442  74.814 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     2.953613   5.558039   0.531 0.595652    
## rank(MR1)       0.097392   0.041082   2.371 0.018593 *  
## rank(MR2)       0.082199   0.022388   3.671 0.000301 ***
## rank(MR3)       0.004384   0.024329   0.180 0.857143    
## conditionrobot -1.471971   2.890813  -0.509 0.611114    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.15 on 227 degrees of freedom
## Multiple R-squared:  0.1323, Adjusted R-squared:  0.117 
## F-statistic: 8.653 on 4 and 227 DF,  p-value: 1.619e-06
## 
## Call:
## lm(formula = concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3)) * 
##     condition, data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.034 -16.302  -5.349  11.421  74.219 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              -1.027891   8.105578  -0.127 0.899202    
## rank(MR1)                 0.109456   0.057459   1.905 0.058069 .  
## rank(MR2)                 0.095008   0.027387   3.469 0.000626 ***
## rank(MR3)                -0.005839   0.035403  -0.165 0.869140    
## conditionrobot           -1.475383   8.105578  -0.182 0.855731    
## rank(MR1):conditionrobot -0.029772   0.057459  -0.518 0.604872    
## rank(MR2):conditionrobot  0.016899   0.027387   0.617 0.537815    
## rank(MR3):conditionrobot  0.019313   0.035403   0.546 0.585941    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.26 on 224 degrees of freedom
## Multiple R-squared:  0.1352, Adjusted R-squared:  0.1082 
## F-statistic: 5.002 on 7 and 224 DF,  p-value: 2.776e-05
## 
## Call:
## lm(formula = concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3) + 
##     condition)^2, data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.501 -15.142  -6.292  12.595  71.322 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              -2.700e+01  1.674e+01  -1.613  0.10813   
## rank(MR1)                 2.386e-01  1.487e-01   1.605  0.10988   
## rank(MR2)                 2.535e-01  9.641e-02   2.629  0.00916 **
## rank(MR3)                 1.334e-01  1.095e-01   1.218  0.22436   
## conditionrobot           -1.065e+00  9.916e+00  -0.107  0.91453   
## rank(MR1):rank(MR2)      -5.417e-04  8.297e-04  -0.653  0.51453   
## rank(MR1):rank(MR3)      -5.129e-04  6.595e-04  -0.778  0.43756   
## rank(MR1):conditionrobot -1.286e-02  6.472e-02  -0.199  0.84270   
## rank(MR2):rank(MR3)      -6.356e-04  4.889e-04  -1.300  0.19496   
## rank(MR2):conditionrobot  7.947e-03  5.537e-02   0.144  0.88601   
## rank(MR3):conditionrobot  4.267e-03  5.730e-02   0.074  0.94070   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.18 on 221 degrees of freedom
## Multiple R-squared:  0.1528, Adjusted R-squared:  0.1145 
## F-statistic: 3.987 on 10 and 221 DF,  p-value: 5.01e-05
## 
## Call:
## lm(formula = concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3))^2 * 
##     condition, data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -42.824 -14.748  -5.485  12.081  69.684 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)                        -3.021e+01  2.069e+01  -1.460   0.1456
## rank(MR1)                           1.940e-01  1.615e-01   1.201   0.2310
## rank(MR2)                           2.964e-01  1.314e-01   2.257   0.0250
## rank(MR3)                           8.293e-02  1.367e-01   0.607   0.5446
## conditionrobot                      2.971e+01  2.069e+01   1.436   0.1525
## rank(MR1):rank(MR2)                -5.570e-04  9.374e-04  -0.594   0.5530
## rank(MR1):rank(MR3)                -4.646e-04  6.745e-04  -0.689   0.4917
## rank(MR2):rank(MR3)                -1.573e-04  5.681e-04  -0.277   0.7822
## rank(MR1):conditionrobot           -3.059e-01  1.615e-01  -1.894   0.0595
## rank(MR2):conditionrobot           -2.012e-01  1.314e-01  -1.532   0.1271
## rank(MR3):conditionrobot            1.935e-02  1.367e-01   0.142   0.8875
## rank(MR1):rank(MR2):conditionrobot  1.935e-03  9.374e-04   2.064   0.0402
## rank(MR1):rank(MR3):conditionrobot  2.734e-04  6.745e-04   0.405   0.6857
## rank(MR2):rank(MR3):conditionrobot -4.397e-04  5.681e-04  -0.774   0.4398
##                                     
## (Intercept)                         
## rank(MR1)                           
## rank(MR2)                          *
## rank(MR3)                           
## conditionrobot                      
## rank(MR1):rank(MR2)                 
## rank(MR1):rank(MR3)                 
## rank(MR2):rank(MR3)                 
## rank(MR1):conditionrobot           .
## rank(MR2):conditionrobot            
## rank(MR3):conditionrobot            
## rank(MR1):rank(MR2):conditionrobot *
## rank(MR1):rank(MR3):conditionrobot  
## rank(MR2):rank(MR3):conditionrobot  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.11 on 218 degrees of freedom
## Multiple R-squared:  0.1693, Adjusted R-squared:  0.1198 
## F-statistic: 3.418 on 13 and 218 DF,  p-value: 8.086e-05
## 
## Call:
## lm(formula = concern_general ~ ((rank(MR1) + rank(MR2) + rank(MR3))^3) * 
##     condition, data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -44.620 -14.770  -4.455  12.244  70.147 
## 
## Coefficients:
##                                                Estimate Std. Error t value
## (Intercept)                                  -6.366e+01  3.263e+01  -1.951
## rank(MR1)                                     6.738e-01  3.274e-01   2.058
## rank(MR2)                                     5.181e-01  2.172e-01   2.385
## rank(MR3)                                     2.617e-01  2.724e-01   0.961
## conditionrobot                               -1.376e+01  3.263e+01  -0.422
## rank(MR1):rank(MR2)                          -3.692e-03  2.102e-03  -1.756
## rank(MR1):rank(MR3)                          -3.426e-03  2.150e-03  -1.593
## rank(MR2):rank(MR3)                          -1.350e-03  1.800e-03  -0.750
## rank(MR1):conditionrobot                      2.284e-01  3.274e-01   0.698
## rank(MR2):conditionrobot                      9.237e-02  2.172e-01   0.425
## rank(MR3):conditionrobot                      3.337e-01  2.724e-01   1.225
## rank(MR1):rank(MR2):rank(MR3)                 1.968e-05  1.408e-05   1.398
## rank(MR1):rank(MR2):conditionrobot           -1.587e-03  2.102e-03  -0.755
## rank(MR1):rank(MR3):conditionrobot           -3.377e-03  2.150e-03  -1.570
## rank(MR2):rank(MR3):conditionrobot           -2.636e-03  1.800e-03  -1.465
## rank(MR1):rank(MR2):rank(MR3):conditionrobot  2.479e-05  1.408e-05   1.760
##                                              Pr(>|t|)  
## (Intercept)                                    0.0524 .
## rank(MR1)                                      0.0408 *
## rank(MR2)                                      0.0179 *
## rank(MR3)                                      0.3377  
## conditionrobot                                 0.6736  
## rank(MR1):rank(MR2)                            0.0805 .
## rank(MR1):rank(MR3)                            0.1126  
## rank(MR2):rank(MR3)                            0.4539  
## rank(MR1):conditionrobot                       0.4860  
## rank(MR2):conditionrobot                       0.6711  
## rank(MR3):conditionrobot                       0.2220  
## rank(MR1):rank(MR2):rank(MR3)                  0.1636  
## rank(MR1):rank(MR2):conditionrobot             0.4511  
## rank(MR1):rank(MR3):conditionrobot             0.1178  
## rank(MR2):rank(MR3):conditionrobot             0.1444  
## rank(MR1):rank(MR2):rank(MR3):conditionrobot   0.0798 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.03 on 216 degrees of freedom
## Multiple R-squared:  0.1827, Adjusted R-squared:  0.126 
## F-statistic: 3.219 on 15 and 216 DF,  p-value: 8.043e-05
## 
## Call:
## lm(formula = concern_general ~ ((rank(MR1) + rank(MR2) + rank(MR3) + 
##     condition)^3), data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.483 -14.858  -5.045  11.512  68.925 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)                        -4.563e+01  3.113e+01  -1.466    0.144
## rank(MR1)                           3.337e-01  2.656e-01   1.256    0.210
## rank(MR2)                           4.035e-01  2.082e-01   1.938    0.054
## rank(MR3)                           2.400e-01  2.735e-01   0.878    0.381
## conditionrobot                      3.069e+01  2.077e+01   1.478    0.141
## rank(MR1):rank(MR2)                -1.498e-03  1.701e-03  -0.881    0.380
## rank(MR1):rank(MR3)                -1.648e-03  1.908e-03  -0.864    0.389
## rank(MR1):conditionrobot           -2.554e-01  1.787e-01  -1.429    0.155
## rank(MR2):rank(MR3)                -1.296e-03  1.808e-03  -0.717    0.474
## rank(MR2):conditionrobot           -2.116e-01  1.325e-01  -1.597    0.112
## rank(MR3):conditionrobot           -4.514e-02  1.679e-01  -0.269    0.788
## rank(MR1):rank(MR2):rank(MR3)       8.345e-06  1.258e-05   0.663    0.508
## rank(MR1):rank(MR2):conditionrobot  1.627e-03  1.047e-03   1.554    0.122
## rank(MR1):rank(MR3):conditionrobot  2.162e-04  6.808e-04   0.318    0.751
## rank(MR2):rank(MR3):conditionrobot  6.266e-05  9.472e-04   0.066    0.947
##                                     
## (Intercept)                         
## rank(MR1)                           
## rank(MR2)                          .
## rank(MR3)                           
## conditionrobot                      
## rank(MR1):rank(MR2)                 
## rank(MR1):rank(MR3)                 
## rank(MR1):conditionrobot            
## rank(MR2):rank(MR3)                 
## rank(MR2):conditionrobot            
## rank(MR3):conditionrobot            
## rank(MR1):rank(MR2):rank(MR3)       
## rank(MR1):rank(MR2):conditionrobot  
## rank(MR1):rank(MR3):conditionrobot  
## rank(MR2):rank(MR3):conditionrobot  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.14 on 217 degrees of freedom
## Multiple R-squared:  0.171,  Adjusted R-squared:  0.1175 
## F-statistic: 3.197 on 14 and 217 DF,  p-value: 0.0001332

## Analysis of Variance Table
## 
## Model 1: concern_general ~ log(MR1 + 10) + log(MR2 + 10) + log(MR3 + 10) + 
##     condition
## Model 2: concern_general ~ (log(MR1 + 10) + log(MR2 + 10) + log(MR3 + 
##     10)) * condition
## Model 3: concern_general ~ (log(MR1 + 10) + log(MR2 + 10) + log(MR3 + 
##     10) + condition)^2
## Model 4: concern_general ~ (log(MR1 + 10) + log(MR2 + 10) + log(MR3 + 
##     10))^2 * condition
## Model 5: concern_general ~ ((log(MR1 + 10) + log(MR2 + 10) + log(MR3 + 
##     10))^3) * condition
## Model 6: concern_general ~ ((log(MR1 + 10) + log(MR2 + 10) + log(MR3 + 
##     10) + condition)^3)
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    226 110999                                
## 2    223 110898  3     100.9 0.0709 0.975439   
## 3    220 104136  3    6761.9 4.7508 0.003143 **
## 4    217 103705  3     431.1 0.3029 0.823277   
## 5    215 102004  2    1701.3 1.7930 0.168947   
## 6    216 102349 -1    -345.9 0.7290 0.394157   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lm(formula = concern_general ~ log(MR1 + 10) + log(MR2 + 10) + 
##     log(MR3 + 10) + condition, data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -42.395 -16.078  -4.117  10.382  73.687 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -268.4229    75.2895  -3.565 0.000444 ***
## log(MR1 + 10)    67.4818    33.4439   2.018 0.044799 *  
## log(MR2 + 10)    61.0595    16.5333   3.693 0.000278 ***
## log(MR3 + 10)    -0.9552    17.2603  -0.055 0.955918    
## conditionrobot   -0.1739     3.4054  -0.051 0.959320    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.16 on 226 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1324, Adjusted R-squared:  0.117 
## F-statistic:  8.62 on 4 and 226 DF,  p-value: 1.716e-06
## 
## Call:
## lm(formula = concern_general ~ (log(MR1 + 10) + log(MR2 + 10) + 
##     log(MR3 + 10)) * condition, data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.800 -16.129  -4.158  10.312  73.740 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                  -291.035     98.368  -2.959  0.00342 **
## log(MR1 + 10)                  83.143     63.375   1.312  0.19089   
## log(MR2 + 10)                  54.180     27.012   2.006  0.04609 * 
## log(MR3 + 10)                   1.002     28.087   0.036  0.97158   
## conditionrobot                -42.589     98.368  -0.433  0.66547   
## log(MR1 + 10):conditionrobot   24.415     63.375   0.385  0.70042   
## log(MR2 + 10):conditionrobot   -4.795     27.012  -0.178  0.85926   
## log(MR3 + 10):conditionrobot   -0.614     28.087  -0.022  0.98258   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.3 on 223 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1332, Adjusted R-squared:  0.106 
## F-statistic: 4.894 on 7 and 223 DF,  p-value: 3.698e-05
## 
## Call:
## lm(formula = concern_general ~ (log(MR1 + 10) + log(MR2 + 10) + 
##     log(MR3 + 10) + condition)^2, data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -48.752 -14.595  -4.393  11.230  68.947 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -6093.82    2458.18  -2.479 0.013927 *  
## log(MR1 + 10)                 3021.78    1132.58   2.668 0.008197 ** 
## log(MR2 + 10)                 2713.99     789.50   3.438 0.000702 ***
## log(MR3 + 10)                 -643.70     956.62  -0.673 0.501722    
## conditionrobot                 -26.21      98.37  -0.266 0.790166    
## log(MR1 + 10):log(MR2 + 10)  -1334.25     382.69  -3.486 0.000591 ***
## log(MR1 + 10):log(MR3 + 10)     95.48     286.67   0.333 0.739401    
## log(MR1 + 10):conditionrobot   112.06      69.38   1.615 0.107697    
## log(MR2 + 10):log(MR3 + 10)    183.27     290.03   0.632 0.528104    
## log(MR2 + 10):conditionrobot  -109.43      41.71  -2.624 0.009301 ** 
## log(MR3 + 10):conditionrobot    13.55      36.35   0.373 0.709723    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21.76 on 220 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.186,  Adjusted R-squared:  0.149 
## F-statistic: 5.028 on 10 and 220 DF,  p-value: 1.384e-06
## 
## Call:
## lm(formula = concern_general ~ (log(MR1 + 10) + log(MR2 + 10) + 
##     log(MR3 + 10))^2 * condition, data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -48.380 -14.461  -4.933  11.436  69.285 
## 
## Coefficients:
##                                            Estimate Std. Error t value
## (Intercept)                                -8278.11    4217.31  -1.963
## log(MR1 + 10)                               3685.53    2034.84   1.811
## log(MR2 + 10)                               3526.34    1242.98   2.837
## log(MR3 + 10)                               -234.07    1525.96  -0.153
## conditionrobot                             -2958.42    4217.31  -0.701
## log(MR1 + 10):log(MR2 + 10)                -1559.42     451.98  -3.450
## log(MR1 + 10):log(MR3 + 10)                   45.01     729.76   0.062
## log(MR2 + 10):log(MR3 + 10)                   52.42     367.99   0.142
## log(MR1 + 10):conditionrobot                 871.50    2034.84   0.428
## log(MR2 + 10):conditionrobot                 935.32    1242.98   0.752
## log(MR3 + 10):conditionrobot                 747.53    1525.96   0.490
## log(MR1 + 10):log(MR2 + 10):conditionrobot  -226.85     451.98  -0.502
## log(MR1 + 10):log(MR3 + 10):conditionrobot   -91.44     729.76  -0.125
## log(MR2 + 10):log(MR3 + 10):conditionrobot  -230.42     367.99  -0.626
##                                            Pr(>|t|)    
## (Intercept)                                0.050937 .  
## log(MR1 + 10)                              0.071491 .  
## log(MR2 + 10)                              0.004985 ** 
## log(MR3 + 10)                              0.878230    
## conditionrobot                             0.483746    
## log(MR1 + 10):log(MR2 + 10)                0.000673 ***
## log(MR1 + 10):log(MR3 + 10)                0.950872    
## log(MR2 + 10):log(MR3 + 10)                0.886867    
## log(MR1 + 10):conditionrobot               0.668865    
## log(MR2 + 10):conditionrobot               0.452579    
## log(MR3 + 10):conditionrobot               0.624719    
## log(MR1 + 10):log(MR2 + 10):conditionrobot 0.616244    
## log(MR1 + 10):log(MR3 + 10):conditionrobot 0.900405    
## log(MR2 + 10):log(MR3 + 10):conditionrobot 0.531875    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21.86 on 217 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1894, Adjusted R-squared:  0.1408 
## F-statistic:   3.9 on 13 and 217 DF,  p-value: 1.09e-05
## 
## Call:
## lm(formula = concern_general ~ ((log(MR1 + 10) + log(MR2 + 10) + 
##     log(MR3 + 10))^3) * condition, data = d_moral_merged)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -42.07 -15.15  -4.49  10.45  68.47 
## 
## Coefficients:
##                                                          Estimate
## (Intercept)                                               -202917
## log(MR1 + 10)                                               89596
## log(MR2 + 10)                                               87469
## log(MR3 + 10)                                               84278
## conditionrobot                                            -100257
## log(MR1 + 10):log(MR2 + 10)                                -38599
## log(MR1 + 10):log(MR3 + 10)                                -37247
## log(MR2 + 10):log(MR3 + 10)                                -36390
## log(MR1 + 10):conditionrobot                                45842
## log(MR2 + 10):conditionrobot                                42247
## log(MR3 + 10):conditionrobot                                42505
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10)                   16076
## log(MR1 + 10):log(MR2 + 10):conditionrobot                 -19331
## log(MR1 + 10):log(MR3 + 10):conditionrobot                 -19411
## log(MR2 + 10):log(MR3 + 10):conditionrobot                 -17951
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10):conditionrobot     8203
##                                                          Std. Error
## (Intercept)                                                  115903
## log(MR1 + 10)                                                 51666
## log(MR2 + 10)                                                 49811
## log(MR3 + 10)                                                 50196
## conditionrobot                                               115903
## log(MR1 + 10):log(MR2 + 10)                                   22191
## log(MR1 + 10):log(MR3 + 10)                                   22371
## log(MR2 + 10):log(MR3 + 10)                                   21570
## log(MR1 + 10):conditionrobot                                  51666
## log(MR2 + 10):conditionrobot                                  49811
## log(MR3 + 10):conditionrobot                                  50196
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10)                      9607
## log(MR1 + 10):log(MR2 + 10):conditionrobot                    22191
## log(MR1 + 10):log(MR3 + 10):conditionrobot                    22371
## log(MR2 + 10):log(MR3 + 10):conditionrobot                    21570
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10):conditionrobot       9607
##                                                          t value Pr(>|t|)
## (Intercept)                                               -1.751   0.0814
## log(MR1 + 10)                                              1.734   0.0843
## log(MR2 + 10)                                              1.756   0.0805
## log(MR3 + 10)                                              1.679   0.0946
## conditionrobot                                            -0.865   0.3880
## log(MR1 + 10):log(MR2 + 10)                               -1.739   0.0834
## log(MR1 + 10):log(MR3 + 10)                               -1.665   0.0974
## log(MR2 + 10):log(MR3 + 10)                               -1.687   0.0930
## log(MR1 + 10):conditionrobot                               0.887   0.3759
## log(MR2 + 10):conditionrobot                               0.848   0.3973
## log(MR3 + 10):conditionrobot                               0.847   0.3981
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10)                  1.673   0.0957
## log(MR1 + 10):log(MR2 + 10):conditionrobot                -0.871   0.3847
## log(MR1 + 10):log(MR3 + 10):conditionrobot                -0.868   0.3865
## log(MR2 + 10):log(MR3 + 10):conditionrobot                -0.832   0.4062
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10):conditionrobot   0.854   0.3942
##                                                           
## (Intercept)                                              .
## log(MR1 + 10)                                            .
## log(MR2 + 10)                                            .
## log(MR3 + 10)                                            .
## conditionrobot                                            
## log(MR1 + 10):log(MR2 + 10)                              .
## log(MR1 + 10):log(MR3 + 10)                              .
## log(MR2 + 10):log(MR3 + 10)                              .
## log(MR1 + 10):conditionrobot                              
## log(MR2 + 10):conditionrobot                              
## log(MR3 + 10):conditionrobot                              
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10)                .
## log(MR1 + 10):log(MR2 + 10):conditionrobot                
## log(MR1 + 10):log(MR3 + 10):conditionrobot                
## log(MR2 + 10):log(MR3 + 10):conditionrobot                
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10):conditionrobot  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21.78 on 215 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2027, Adjusted R-squared:  0.1471 
## F-statistic: 3.644 on 15 and 215 DF,  p-value: 1.145e-05
## 
## Call:
## lm(formula = concern_general ~ ((log(MR1 + 10) + log(MR2 + 10) + 
##     log(MR3 + 10) + condition)^3), data = d_moral_merged)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.863 -14.966  -5.071  10.842  68.319 
## 
## Coefficients:
##                                             Estimate Std. Error t value
## (Intercept)                                -122654.7    67756.7  -1.810
## log(MR1 + 10)                                53355.2    29437.6   1.812
## log(MR2 + 10)                                53115.8    29346.4   1.810
## log(MR3 + 10)                                49624.8    29518.7   1.681
## conditionrobot                               -1365.5     4303.7  -0.317
## log(MR1 + 10):log(MR2 + 10)                 -23088.3    12737.1  -1.813
## log(MR1 + 10):log(MR3 + 10)                 -21596.7    12816.5  -1.685
## log(MR1 + 10):conditionrobot                  1764.8     2093.9   0.843
## log(MR2 + 10):log(MR3 + 10)                 -21562.7    12785.4  -1.687
## log(MR2 + 10):conditionrobot                  -265.1     1426.7  -0.186
## log(MR3 + 10):conditionrobot                  -329.9     1647.6  -0.200
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10)     9379.6     5545.8   1.691
## log(MR1 + 10):log(MR2 + 10):conditionrobot    -387.6      460.0  -0.843
## log(MR1 + 10):log(MR3 + 10):conditionrobot    -320.3      739.1  -0.433
## log(MR2 + 10):log(MR3 + 10):conditionrobot     460.0      548.5   0.839
##                                            Pr(>|t|)  
## (Intercept)                                  0.0717 .
## log(MR1 + 10)                                0.0713 .
## log(MR2 + 10)                                0.0717 .
## log(MR3 + 10)                                0.0942 .
## conditionrobot                               0.7513  
## log(MR1 + 10):log(MR2 + 10)                  0.0713 .
## log(MR1 + 10):log(MR3 + 10)                  0.0934 .
## log(MR1 + 10):conditionrobot                 0.4003  
## log(MR2 + 10):log(MR3 + 10)                  0.0931 .
## log(MR2 + 10):conditionrobot                 0.8528  
## log(MR3 + 10):conditionrobot                 0.8415  
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10)    0.0922 .
## log(MR1 + 10):log(MR2 + 10):conditionrobot   0.4004  
## log(MR1 + 10):log(MR3 + 10):conditionrobot   0.6652  
## log(MR2 + 10):log(MR3 + 10):conditionrobot   0.4026  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21.77 on 216 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:    0.2,  Adjusted R-squared:  0.1481 
## F-statistic: 3.857 on 14 and 216 DF,  p-value: 7.403e-06
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

Start messing with specific moral concern

## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Data: d_moral_merged3
## Models:
## r01: concern_score ~ concern_type + (1 | subid) + (1 | concern_item)
## r02: concern_score ~ concern_type + scale(rank(MR1), scale = F) + 
## r02:     scale(rank(MR2), scale = F) + scale(rank(MR3), scale = F) + 
## r02:     (1 | subid) + (1 | concern_item)
## r03: concern_score ~ concern_type + scale(rank(MR1), scale = F) + 
## r03:     scale(rank(MR2), scale = F) + scale(rank(MR3), scale = F) + 
## r03:     concern_type:scale(rank(MR1), scale = F) + concern_type:scale(rank(MR2), 
## r03:     scale = F) + concern_type:scale(rank(MR3), scale = F) + (1 | 
## r03:     subid) + (1 | concern_item)
## r04: concern_score ~ (concern_type + scale(rank(MR1), scale = F) + 
## r04:     scale(rank(MR2), scale = F) + scale(rank(MR3), scale = F))^2 + 
## r04:     (1 | subid) + (1 | concern_item)
## r05: concern_score ~ (concern_type + scale(rank(MR1), scale = F) + 
## r05:     scale(rank(MR2), scale = F) + scale(rank(MR3), scale = F))^2 * 
## r05:     condition + (1 | subid) + (1 | concern_item)
##     Df   AIC   BIC logLik deviance   Chisq Chi Df Pr(>Chisq)    
## r01  7 22431 22471 -11208    22417                              
## r02 10 22380 22438 -11180    22360  57.008      3  2.560e-12 ***
## r03 19 22142 22253 -11052    22104 255.217      9  < 2.2e-16 ***
## r04 22 22147 22275 -11052    22103   1.357      3     0.7157    
## r05 41 22090 22329 -11004    22008  95.448     19  3.542e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Linear mixed model fit by REML ['lmerMod']
## Formula: concern_score ~ (concern_type + scale(rank(MR1), scale = F) +  
##     scale(rank(MR2), scale = F) + scale(rank(MR3), scale = F))^2 *  
##     condition + (1 | subid) + (1 | concern_item)
##    Data: d_moral_merged3
## 
## REML criterion at convergence: 22409.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1678 -0.5485  0.0033  0.4347  4.0360 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  subid        (Intercept) 322.181  17.949  
##  concern_item (Intercept)   2.824   1.681  
##  Residual                 283.526  16.838  
## Number of obs: 2526, groups:  subid, 232; concern_item, 11
## 
## Fixed effects:
##                                                                          Estimate
## (Intercept)                                                             1.264e+01
## concern_typephy                                                         9.662e+00
## concern_typesoc                                                        -4.012e+00
## concern_typeper                                                        -1.244e+00
## scale(rank(MR1), scale = F)                                             1.543e-02
## scale(rank(MR2), scale = F)                                             1.645e-02
## scale(rank(MR3), scale = F)                                            -7.207e-03
## conditionrobot                                                          5.505e+00
## concern_typephy:scale(rank(MR1), scale = F)                             7.386e-03
## concern_typesoc:scale(rank(MR1), scale = F)                            -8.172e-03
## concern_typeper:scale(rank(MR1), scale = F)                            -1.565e-03
## concern_typephy:scale(rank(MR2), scale = F)                             2.244e-03
## concern_typesoc:scale(rank(MR2), scale = F)                            -8.029e-04
## concern_typeper:scale(rank(MR2), scale = F)                            -8.776e-04
## concern_typephy:scale(rank(MR3), scale = F)                             1.561e-03
## concern_typesoc:scale(rank(MR3), scale = F)                             3.436e-04
## concern_typeper:scale(rank(MR3), scale = F)                             3.093e-04
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F)                 6.809e-06
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F)                 5.527e-06
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F)                -1.591e-06
## concern_typephy:conditionrobot                                         -2.456e+00
## concern_typesoc:conditionrobot                                          9.224e-01
## concern_typeper:conditionrobot                                         -2.254e+00
## scale(rank(MR1), scale = F):conditionrobot                             -1.318e-02
## scale(rank(MR2), scale = F):conditionrobot                              4.322e-03
## scale(rank(MR3), scale = F):conditionrobot                              8.956e-03
## concern_typephy:scale(rank(MR1), scale = F):conditionrobot             -9.396e-03
## concern_typesoc:scale(rank(MR1), scale = F):conditionrobot              7.024e-03
## concern_typeper:scale(rank(MR1), scale = F):conditionrobot              2.271e-03
## concern_typephy:scale(rank(MR2), scale = F):conditionrobot              4.066e-03
## concern_typesoc:scale(rank(MR2), scale = F):conditionrobot             -4.886e-04
## concern_typeper:scale(rank(MR2), scale = F):conditionrobot             -1.751e-03
## concern_typephy:scale(rank(MR3), scale = F):conditionrobot             -7.092e-04
## concern_typesoc:scale(rank(MR3), scale = F):conditionrobot              8.290e-04
## concern_typeper:scale(rank(MR3), scale = F):conditionrobot             -2.380e-03
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F):conditionrobot  9.163e-06
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F):conditionrobot -3.516e-06
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F):conditionrobot  7.767e-07
##                                                                        Std. Error
## (Intercept)                                                             4.621e+00
## concern_typephy                                                         1.774e+00
## concern_typesoc                                                         1.774e+00
## concern_typeper                                                         1.780e+00
## scale(rank(MR1), scale = F)                                             5.631e-03
## scale(rank(MR2), scale = F)                                             4.476e-03
## scale(rank(MR3), scale = F)                                             4.700e-03
## conditionrobot                                                          4.592e+00
## concern_typephy:scale(rank(MR1), scale = F)                             2.024e-03
## concern_typesoc:scale(rank(MR1), scale = F)                             2.024e-03
## concern_typeper:scale(rank(MR1), scale = F)                             2.030e-03
## concern_typephy:scale(rank(MR2), scale = F)                             9.655e-04
## concern_typesoc:scale(rank(MR2), scale = F)                             9.645e-04
## concern_typeper:scale(rank(MR2), scale = F)                             9.711e-04
## concern_typephy:scale(rank(MR3), scale = F)                             1.246e-03
## concern_typesoc:scale(rank(MR3), scale = F)                             1.245e-03
## concern_typeper:scale(rank(MR3), scale = F)                             1.248e-03
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F)                 6.539e-06
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F)                 4.705e-06
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F)                 3.963e-06
## concern_typephy:conditionrobot                                          1.553e+00
## concern_typesoc:conditionrobot                                          1.553e+00
## concern_typeper:conditionrobot                                          1.560e+00
## scale(rank(MR1), scale = F):conditionrobot                              5.631e-03
## scale(rank(MR2), scale = F):conditionrobot                              4.476e-03
## scale(rank(MR3), scale = F):conditionrobot                              4.700e-03
## concern_typephy:scale(rank(MR1), scale = F):conditionrobot              2.024e-03
## concern_typesoc:scale(rank(MR1), scale = F):conditionrobot              2.024e-03
## concern_typeper:scale(rank(MR1), scale = F):conditionrobot              2.030e-03
## concern_typephy:scale(rank(MR2), scale = F):conditionrobot              9.655e-04
## concern_typesoc:scale(rank(MR2), scale = F):conditionrobot              9.645e-04
## concern_typeper:scale(rank(MR2), scale = F):conditionrobot              9.711e-04
## concern_typephy:scale(rank(MR3), scale = F):conditionrobot              1.246e-03
## concern_typesoc:scale(rank(MR3), scale = F):conditionrobot              1.245e-03
## concern_typeper:scale(rank(MR3), scale = F):conditionrobot              1.248e-03
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F):conditionrobot  6.539e-06
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F):conditionrobot  4.705e-06
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F):conditionrobot  3.963e-06
##                                                                        t value
## (Intercept)                                                              2.736
## concern_typephy                                                          5.446
## concern_typesoc                                                         -2.262
## concern_typeper                                                         -0.699
## scale(rank(MR1), scale = F)                                              2.740
## scale(rank(MR2), scale = F)                                              3.675
## scale(rank(MR3), scale = F)                                             -1.533
## conditionrobot                                                           1.199
## concern_typephy:scale(rank(MR1), scale = F)                              3.649
## concern_typesoc:scale(rank(MR1), scale = F)                             -4.038
## concern_typeper:scale(rank(MR1), scale = F)                             -0.771
## concern_typephy:scale(rank(MR2), scale = F)                              2.325
## concern_typesoc:scale(rank(MR2), scale = F)                             -0.832
## concern_typeper:scale(rank(MR2), scale = F)                             -0.904
## concern_typephy:scale(rank(MR3), scale = F)                              1.252
## concern_typesoc:scale(rank(MR3), scale = F)                              0.276
## concern_typeper:scale(rank(MR3), scale = F)                              0.248
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F)                  1.041
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F)                  1.175
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F)                 -0.401
## concern_typephy:conditionrobot                                          -1.582
## concern_typesoc:conditionrobot                                           0.594
## concern_typeper:conditionrobot                                          -1.445
## scale(rank(MR1), scale = F):conditionrobot                              -2.341
## scale(rank(MR2), scale = F):conditionrobot                               0.966
## scale(rank(MR3), scale = F):conditionrobot                               1.905
## concern_typephy:scale(rank(MR1), scale = F):conditionrobot              -4.642
## concern_typesoc:scale(rank(MR1), scale = F):conditionrobot               3.471
## concern_typeper:scale(rank(MR1), scale = F):conditionrobot               1.119
## concern_typephy:scale(rank(MR2), scale = F):conditionrobot               4.211
## concern_typesoc:scale(rank(MR2), scale = F):conditionrobot              -0.507
## concern_typeper:scale(rank(MR2), scale = F):conditionrobot              -1.804
## concern_typephy:scale(rank(MR3), scale = F):conditionrobot              -0.569
## concern_typesoc:scale(rank(MR3), scale = F):conditionrobot               0.666
## concern_typeper:scale(rank(MR3), scale = F):conditionrobot              -1.908
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F):conditionrobot   1.401
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F):conditionrobot  -0.747
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F):conditionrobot   0.196
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling

Plots with specific moral concern

## Warning: Removed 37 rows containing non-finite values (stat_smooth).
## Warning: Removed 37 rows containing missing values (geom_point).

## Warning: Removed 37 rows containing non-finite values (stat_smooth).
## Warning: Removed 37 rows containing missing values (geom_point).

## Warning: Removed 37 rows containing non-finite values (stat_smooth).
## Warning: Removed 37 rows containing missing values (geom_point).

## Warning: Removed 37 rows containing non-finite values (stat_smooth).
## Warning: Removed 37 rows containing missing values (geom_point).

## Warning: Removed 37 rows containing non-finite values (stat_smooth).
## Warning: Removed 37 rows containing missing values (geom_point).

## Warning: Removed 37 rows containing non-finite values (stat_smooth).
## Warning: Removed 37 rows containing missing values (geom_point).